code: def frexp(x, out1=None, out2=None, out=None, where=None, **kwargs):
      """
      Decompose the elements of x into mantissa and twos exponent.
  
      Returns (`mantissa`, `exponent`), where `x = mantissa * 2**exponent``.
      The mantissa is lies in the open interval(-1, 1), while the twos
      exponent is a signed integer.
  
      Parameters
      ----------
      x : array_like
          Tensor of numbers to be decomposed.
      out1 : Tensor, optional
          Output tensor for the mantissa. Must have the same shape as `x`.
      out2 : Tensor, optional
          Output tensor for the exponent. Must have the same shape as `x`.
      out : Tensor, None, or tuple of Tensor and None, optional
          A location into which the result is stored. If provided, it must have
          a shape that the inputs broadcast to. If not provided or `None`,
          a freshly-allocated tensor is returned. A tuple (possible only as a
          keyword argument) must have length equal to the number of outputs.
      where : array_like, optional
          Values of True indicate to calculate the ufunc at that position, values
          of False indicate to leave the value in the output alone.
      **kwargs
  
      Returns
      -------
      (mantissa, exponent) : tuple of tensors, (float, int)
          `mantissa` is a float array with values between -1 and 1.
          `exponent` is an int array which represents the exponent of 2.
  
      See Also
      --------
      ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`.
  
      Notes
      -----
      Complex dtypes are not supported, they will raise a TypeError.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> x = mt.arange(9)
      >>> y1, y2 = mt.frexp(x)
  
      >>> y1_result, y2_result = mt.ExecutableTuple([y1, y2]).execute()
      >>> y1_result
      array([ 0.   ,  0.5  ,  0.5  ,  0.75 ,  0.5  ,  0.625,  0.75 ,  0.875,
              0.5  ])
      >>> y2_result
      array([0, 1, 2, 2, 3, 3, 3, 3, 4])
      >>> (y1 * 2**y2).execute()
      array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.])
      """
      op = TensorFrexp(**kwargs)
      return op(x, out1=out1, out2=out2, out=out, where=where)
  
file_path: mars/mars/tensor/arithmetic/frexp.py
imports: import numpy
  from ...None import opcodes
  from ..array_utils import device, as_same_device
  from .core import TensorOutBinOp
label_desc: Use image node_id 1 for calling a global function with example usage: frexp(x, out1, out2, out, where) and returns: op